Overview

Dataset statistics

Number of variables17
Number of observations8392320
Missing cells6088797
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 GiB
Average record size in memory136.0 B

Variable types

Categorical2
Text1
Numeric14

Alerts

bx_gse is highly overall correlated with bx_gsmHigh correlation
bx_gsm is highly overall correlated with bx_gseHigh correlation
by_gse is highly overall correlated with by_gsm and 2 other fieldsHigh correlation
by_gsm is highly overall correlated with by_gse and 2 other fieldsHigh correlation
bz_gse is highly overall correlated with bz_gsm and 2 other fieldsHigh correlation
bz_gsm is highly overall correlated with bz_gse and 2 other fieldsHigh correlation
period is highly overall correlated with sourceHigh correlation
phi_gse is highly overall correlated with by_gse and 2 other fieldsHigh correlation
phi_gsm is highly overall correlated with by_gse and 2 other fieldsHigh correlation
source is highly overall correlated with periodHigh correlation
speed is highly overall correlated with temperatureHigh correlation
temperature is highly overall correlated with speedHigh correlation
theta_gse is highly overall correlated with bz_gse and 2 other fieldsHigh correlation
theta_gsm is highly overall correlated with bz_gse and 2 other fieldsHigh correlation
bx_gse has 325888 (3.9%) missing valuesMissing
by_gse has 325888 (3.9%) missing valuesMissing
bz_gse has 325888 (3.9%) missing valuesMissing
theta_gse has 325888 (3.9%) missing valuesMissing
phi_gse has 326388 (3.9%) missing valuesMissing
bx_gsm has 325888 (3.9%) missing valuesMissing
by_gsm has 325888 (3.9%) missing valuesMissing
bz_gsm has 325888 (3.9%) missing valuesMissing
theta_gsm has 325888 (3.9%) missing valuesMissing
phi_gsm has 326388 (3.9%) missing valuesMissing
bt has 325888 (3.9%) missing valuesMissing
density has 684890 (8.2%) missing valuesMissing
speed has 689555 (8.2%) missing valuesMissing
temperature has 811768 (9.7%) missing valuesMissing
source has 316816 (3.8%) missing valuesMissing

Reproduction

Analysis started2024-01-23 07:44:15.659954
Analysis finished2024-01-23 08:23:18.451716
Duration39 minutes and 2.79 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

period
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.0 MiB
train_c
3507840 
train_b
3155040 
train_a
1729440 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters58746240
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtrain_a
2nd rowtrain_a
3rd rowtrain_a
4th rowtrain_a
5th rowtrain_a

Common Values

ValueCountFrequency (%)
train_c 3507840
41.8%
train_b 3155040
37.6%
train_a 1729440
20.6%

Length

2024-01-23T13:53:19.179129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-23T13:53:19.631128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
train_c 3507840
41.8%
train_b 3155040
37.6%
train_a 1729440
20.6%

Most occurring characters

ValueCountFrequency (%)
a 10121760
17.2%
t 8392320
14.3%
r 8392320
14.3%
i 8392320
14.3%
n 8392320
14.3%
_ 8392320
14.3%
c 3507840
 
6.0%
b 3155040
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50353920
85.7%
Connector Punctuation 8392320
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10121760
20.1%
t 8392320
16.7%
r 8392320
16.7%
i 8392320
16.7%
n 8392320
16.7%
c 3507840
 
7.0%
b 3155040
 
6.3%
Connector Punctuation
ValueCountFrequency (%)
_ 8392320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50353920
85.7%
Common 8392320
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10121760
20.1%
t 8392320
16.7%
r 8392320
16.7%
i 8392320
16.7%
n 8392320
16.7%
c 3507840
 
7.0%
b 3155040
 
6.3%
Common
ValueCountFrequency (%)
_ 8392320
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58746240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10121760
17.2%
t 8392320
14.3%
r 8392320
14.3%
i 8392320
14.3%
n 8392320
14.3%
_ 8392320
14.3%
c 3507840
 
6.0%
b 3155040
 
5.4%
Distinct3507840
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Memory size64.0 MiB
2024-01-23T13:53:21.102047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length18
Median length17
Mean length17.42862
Min length15

Characters and Unicode

Total characters146266560
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique352800 ?
Unique (%)4.2%

Sample

1st row0 days 00:00:00
2nd row0 days 00:01:00
3rd row0 days 00:02:00
4th row0 days 00:03:00
5th row0 days 00:04:00
ValueCountFrequency (%)
days 8392320
33.3%
04:16:00 5828
 
< 0.1%
04:02:00 5828
 
< 0.1%
03:53:00 5828
 
< 0.1%
03:54:00 5828
 
< 0.1%
03:55:00 5828
 
< 0.1%
03:56:00 5828
 
< 0.1%
03:57:00 5828
 
< 0.1%
03:58:00 5828
 
< 0.1%
03:59:00 5828
 
< 0.1%
Other values (3867) 16732188
66.5%
2024-01-23T13:53:21.869197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 25938672
17.7%
16784640
11.5%
: 16784640
11.5%
1 12781392
8.7%
d 8392320
 
5.7%
a 8392320
 
5.7%
y 8392320
 
5.7%
s 8392320
 
5.7%
2 8143152
 
5.6%
3 5834352
 
4.0%
Other values (6) 26430432
18.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79128000
54.1%
Lowercase Letter 33569280
23.0%
Space Separator 16784640
 
11.5%
Other Punctuation 16784640
 
11.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25938672
32.8%
1 12781392
16.2%
2 8143152
 
10.3%
3 5834352
 
7.4%
4 5383872
 
6.8%
5 5332032
 
6.7%
8 3931872
 
5.0%
6 3931872
 
5.0%
7 3931872
 
5.0%
9 3918912
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
d 8392320
25.0%
a 8392320
25.0%
y 8392320
25.0%
s 8392320
25.0%
Space Separator
ValueCountFrequency (%)
16784640
100.0%
Other Punctuation
ValueCountFrequency (%)
: 16784640
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 112697280
77.0%
Latin 33569280
 
23.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25938672
23.0%
16784640
14.9%
: 16784640
14.9%
1 12781392
11.3%
2 8143152
 
7.2%
3 5834352
 
5.2%
4 5383872
 
4.8%
5 5332032
 
4.7%
8 3931872
 
3.5%
6 3931872
 
3.5%
Other values (2) 7850784
 
7.0%
Latin
ValueCountFrequency (%)
d 8392320
25.0%
a 8392320
25.0%
y 8392320
25.0%
s 8392320
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 146266560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25938672
17.7%
16784640
11.5%
: 16784640
11.5%
1 12781392
8.7%
d 8392320
 
5.7%
a 8392320
 
5.7%
y 8392320
 
5.7%
s 8392320
 
5.7%
2 8143152
 
5.6%
3 5834352
 
4.0%
Other values (6) 26430432
18.1%

bx_gse
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5758
Distinct (%)0.1%
Missing325888
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean-0.66101671
Minimum-54.63
Maximum55.55
Zeros6788
Zeros (%)0.1%
Negative4500158
Negative (%)53.6%
Memory size64.0 MiB
2024-01-23T13:53:22.219712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-54.63
5-th percentile-6.3
Q1-3.27
median-0.67
Q32.02
95-th percentile4.91
Maximum55.55
Range110.18
Interquartile range (IQR)5.29

Descriptive statistics

Standard deviation3.6697301
Coefficient of variation (CV)-5.5516449
Kurtosis1.872836
Mean-0.66101671
Median Absolute Deviation (MAD)2.64
Skewness-0.12226213
Sum-5332046.4
Variance13.466919
MonotonicityNot monotonic
2024-01-23T13:53:22.537386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.94 8771
 
0.1%
-2.59 8647
 
0.1%
-3.09 8643
 
0.1%
-2.84 8606
 
0.1%
-2.8 8583
 
0.1%
-2.92 8572
 
0.1%
1.96 8566
 
0.1%
-2.83 8564
 
0.1%
-2.34 8564
 
0.1%
2.09 8558
 
0.1%
Other values (5748) 7980358
95.1%
(Missing) 325888
 
3.9%
ValueCountFrequency (%)
-54.63 1
< 0.1%
-54.25 1
< 0.1%
-53.68 1
< 0.1%
-52.87 1
< 0.1%
-51.77 1
< 0.1%
-51.08 1
< 0.1%
-50.21 1
< 0.1%
-49.74 1
< 0.1%
-49.69 1
< 0.1%
-49.61 1
< 0.1%
ValueCountFrequency (%)
55.55 1
< 0.1%
41.13 1
< 0.1%
32.89 1
< 0.1%
32.55 1
< 0.1%
32.51 1
< 0.1%
31.99 2
< 0.1%
31.91 2
< 0.1%
31.9 1
< 0.1%
31.72 1
< 0.1%
31.7 1
< 0.1%

by_gse
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6677
Distinct (%)0.1%
Missing325888
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean0.10978711
Minimum-51.69
Maximum57.6
Zeros7480
Zeros (%)0.1%
Negative3947423
Negative (%)47.0%
Memory size64.0 MiB
2024-01-23T13:53:22.848078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-51.69
5-th percentile-5.94
Q1-2.46
median0.11
Q32.64
95-th percentile6.16
Maximum57.6
Range109.29
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation3.9753398
Coefficient of variation (CV)36.20953
Kurtosis3.2015418
Mean0.10978711
Median Absolute Deviation (MAD)2.55
Skewness0.12733726
Sum885590.29
Variance15.803327
MonotonicityNot monotonic
2024-01-23T13:53:23.121391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.48 8736
 
0.1%
1.23 8716
 
0.1%
1.69 8688
 
0.1%
1.71 8662
 
0.1%
1.73 8658
 
0.1%
1.94 8573
 
0.1%
1.21 8553
 
0.1%
1.19 8529
 
0.1%
1.46 8509
 
0.1%
1.67 8507
 
0.1%
Other values (6667) 7980301
95.1%
(Missing) 325888
 
3.9%
ValueCountFrequency (%)
-51.69 1
< 0.1%
-48.95 1
< 0.1%
-47.63 1
< 0.1%
-47.48 1
< 0.1%
-46.52 1
< 0.1%
-46.18 1
< 0.1%
-46.14 1
< 0.1%
-46.13 1
< 0.1%
-46.1 1
< 0.1%
-46.05 1
< 0.1%
ValueCountFrequency (%)
57.6 1
< 0.1%
57.02 1
< 0.1%
56.96 1
< 0.1%
56.86 1
< 0.1%
56.74 1
< 0.1%
56.67 1
< 0.1%
56.43 1
< 0.1%
55.99 1
< 0.1%
55.88 1
< 0.1%
55.87 1
< 0.1%

bz_gse
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6919
Distinct (%)0.1%
Missing325888
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean-0.022742309
Minimum-55
Maximum74.03
Zeros13396
Zeros (%)0.2%
Negative4011589
Negative (%)47.8%
Memory size64.0 MiB
2024-01-23T13:53:23.424854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-55
5-th percentile-5.12
Q1-1.7
median0.01
Q31.69
95-th percentile5.05
Maximum74.03
Range129.03
Interquartile range (IQR)3.39

Descriptive statistics

Standard deviation3.3519724
Coefficient of variation (CV)-147.38927
Kurtosis8.3485501
Mean-0.022742309
Median Absolute Deviation (MAD)1.7
Skewness-0.30514539
Sum-183449.29
Variance11.235719
MonotonicityNot monotonic
2024-01-23T13:53:23.696309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07 14225
 
0.2%
-0.1 14131
 
0.2%
0.02 14110
 
0.2%
0.08 14058
 
0.2%
0.14 14022
 
0.2%
0.16 14013
 
0.2%
0.2 13963
 
0.2%
-0.08 13944
 
0.2%
0.15 13924
 
0.2%
0.11 13924
 
0.2%
Other values (6909) 7926118
94.4%
(Missing) 325888
 
3.9%
ValueCountFrequency (%)
-55 1
< 0.1%
-54.84 1
< 0.1%
-54.4 1
< 0.1%
-54.27 1
< 0.1%
-54.26 1
< 0.1%
-54.24 1
< 0.1%
-54.09 1
< 0.1%
-53.05 1
< 0.1%
-52.85 1
< 0.1%
-52.49 1
< 0.1%
ValueCountFrequency (%)
74.03 1
< 0.1%
57.3 1
< 0.1%
52.42 1
< 0.1%
51.42 1
< 0.1%
50.89 1
< 0.1%
50.86 1
< 0.1%
49.62 1
< 0.1%
49.61 1
< 0.1%
49.01 1
< 0.1%
48.87 1
< 0.1%

theta_gse
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17958
Distinct (%)0.2%
Missing325888
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean0.15456281
Minimum-89.89
Maximum89.94
Zeros1977
Zeros (%)< 0.1%
Negative4017359
Negative (%)47.9%
Memory size64.0 MiB
2024-01-23T13:53:23.982835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-89.89
5-th percentile-54.03
Q1-21.45
median0.14
Q321.68
95-th percentile54.54
Maximum89.94
Range179.83
Interquartile range (IQR)43.13

Descriptive statistics

Standard deviation32.129161
Coefficient of variation (CV)207.87122
Kurtosis-0.25788384
Mean0.15456281
Median Absolute Deviation (MAD)21.57
Skewness0.0029833965
Sum1246770.4
Variance1032.283
MonotonicityNot monotonic
2024-01-23T13:53:24.230584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1977
 
< 0.1%
-3.71 1140
 
< 0.1%
-2.52 1120
 
< 0.1%
1.79 1115
 
< 0.1%
4.83 1111
 
< 0.1%
-1.07 1111
 
< 0.1%
-2.81 1108
 
< 0.1%
0.07 1108
 
< 0.1%
-0.23 1108
 
< 0.1%
2.96 1100
 
< 0.1%
Other values (17948) 8054434
96.0%
(Missing) 325888
 
3.9%
ValueCountFrequency (%)
-89.89 1
 
< 0.1%
-89.86 2
 
< 0.1%
-89.85 1
 
< 0.1%
-89.82 1
 
< 0.1%
-89.81 2
 
< 0.1%
-89.8 2
 
< 0.1%
-89.79 1
 
< 0.1%
-89.78 2
 
< 0.1%
-89.77 5
< 0.1%
-89.76 2
 
< 0.1%
ValueCountFrequency (%)
89.94 1
 
< 0.1%
89.9 1
 
< 0.1%
89.87 1
 
< 0.1%
89.86 1
 
< 0.1%
89.85 2
< 0.1%
89.8 3
< 0.1%
89.79 2
< 0.1%
89.78 1
 
< 0.1%
89.75 1
 
< 0.1%
89.74 1
 
< 0.1%

phi_gse
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36001
Distinct (%)0.4%
Missing326388
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean194.17589
Minimum0
Maximum360
Zeros330
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size64.0 MiB
2024-01-23T13:53:24.559757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.51
Q1124.07
median176.86
Q3286.42
95-th percentile340.28
Maximum360
Range360
Interquartile range (IQR)162.35

Descriptive statistics

Standard deviation96.628127
Coefficient of variation (CV)0.49763196
Kurtosis-1.0718166
Mean194.17589
Median Absolute Deviation (MAD)77.94
Skewness-0.026555431
Sum1.5662095 × 109
Variance9336.9949
MonotonicityNot monotonic
2024-01-23T13:53:24.849848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135 733
 
< 0.1%
180 554
 
< 0.1%
153.43 540
 
< 0.1%
315 526
 
< 0.1%
134.07 491
 
< 0.1%
146.31 491
 
< 0.1%
138.12 490
 
< 0.1%
144.35 490
 
< 0.1%
138.94 485
 
< 0.1%
134.74 483
 
< 0.1%
Other values (35991) 8060649
96.0%
(Missing) 326388
 
3.9%
ValueCountFrequency (%)
0 330
< 0.1%
0.01 134
< 0.1%
0.02 133
< 0.1%
0.03 123
 
< 0.1%
0.04 147
< 0.1%
0.05 157
< 0.1%
0.06 194
< 0.1%
0.07 162
< 0.1%
0.08 202
< 0.1%
0.09 163
< 0.1%
ValueCountFrequency (%)
360 74
 
< 0.1%
359.99 151
< 0.1%
359.98 136
< 0.1%
359.97 141
< 0.1%
359.96 134
< 0.1%
359.95 174
< 0.1%
359.94 191
< 0.1%
359.93 199
< 0.1%
359.92 176
< 0.1%
359.91 180
< 0.1%

bx_gsm
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5740
Distinct (%)0.1%
Missing325888
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean-0.66086633
Minimum-54.63
Maximum55.54
Zeros6809
Zeros (%)0.1%
Negative4500605
Negative (%)53.6%
Memory size64.0 MiB
2024-01-23T13:53:25.186531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-54.63
5-th percentile-6.3
Q1-3.27
median-0.67
Q32.02
95-th percentile4.91
Maximum55.54
Range110.17
Interquartile range (IQR)5.29

Descriptive statistics

Standard deviation3.6676047
Coefficient of variation (CV)-5.5496922
Kurtosis1.875649
Mean-0.66086633
Median Absolute Deviation (MAD)2.64
Skewness-0.12274573
Sum-5330833.3
Variance13.451324
MonotonicityNot monotonic
2024-01-23T13:53:25.512915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.94 8768
 
0.1%
-2.59 8760
 
0.1%
-3.09 8747
 
0.1%
-2.84 8699
 
0.1%
1.96 8631
 
0.1%
-3.05 8581
 
0.1%
1.69 8550
 
0.1%
-2.8 8549
 
0.1%
2.09 8542
 
0.1%
-3.34 8533
 
0.1%
Other values (5730) 7980072
95.1%
(Missing) 325888
 
3.9%
ValueCountFrequency (%)
-54.63 1
< 0.1%
-54.24 1
< 0.1%
-53.68 1
< 0.1%
-52.86 1
< 0.1%
-51.77 1
< 0.1%
-51.07 1
< 0.1%
-50.2 1
< 0.1%
-49.73 1
< 0.1%
-49.68 1
< 0.1%
-49.6 1
< 0.1%
ValueCountFrequency (%)
55.54 1
< 0.1%
41.13 1
< 0.1%
32.89 1
< 0.1%
32.55 1
< 0.1%
32.52 1
< 0.1%
31.99 2
< 0.1%
31.91 2
< 0.1%
31.9 1
< 0.1%
31.72 1
< 0.1%
31.7 1
< 0.1%

by_gsm
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6636
Distinct (%)0.1%
Missing325888
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean0.088421101
Minimum-52.68
Maximum53.88
Zeros7961
Zeros (%)0.1%
Negative3965981
Negative (%)47.3%
Memory size64.0 MiB
2024-01-23T13:53:25.819369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-52.68
5-th percentile-5.88
Q1-2.38
median0.08
Q32.54
95-th percentile6.03
Maximum53.88
Range106.56
Interquartile range (IQR)4.92

Descriptive statistics

Standard deviation3.9098851
Coefficient of variation (CV)44.218915
Kurtosis3.363619
Mean0.088421101
Median Absolute Deviation (MAD)2.46
Skewness0.11818784
Sum713242.8
Variance15.287202
MonotonicityNot monotonic
2024-01-23T13:53:26.154798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8578
 
0.1%
0.23 8562
 
0.1%
-0.17 8559
 
0.1%
-1.75 8549
 
0.1%
-0.07 8540
 
0.1%
1.75 8529
 
0.1%
-0.01 8525
 
0.1%
1.5 8523
 
0.1%
-0.81 8516
 
0.1%
1.2 8516
 
0.1%
Other values (6626) 7981035
95.1%
(Missing) 325888
 
3.9%
ValueCountFrequency (%)
-52.68 1
< 0.1%
-50.14 1
< 0.1%
-48.91 1
< 0.1%
-47.52 1
< 0.1%
-47.48 1
< 0.1%
-47.35 1
< 0.1%
-46.25 1
< 0.1%
-45.65 1
< 0.1%
-45.57 1
< 0.1%
-45.16 1
< 0.1%
ValueCountFrequency (%)
53.88 1
< 0.1%
52.91 1
< 0.1%
52.46 1
< 0.1%
52.4 1
< 0.1%
52.31 1
< 0.1%
52.17 1
< 0.1%
52.15 1
< 0.1%
52.1 1
< 0.1%
51.86 1
< 0.1%
51.33 1
< 0.1%

bz_gsm
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7010
Distinct (%)0.1%
Missing325888
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean-0.02912843
Minimum-55.69
Maximum72.45
Zeros12539
Zeros (%)0.1%
Negative4021429
Negative (%)47.9%
Memory size64.0 MiB
2024-01-23T13:53:26.443366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-55.69
5-th percentile-5.24
Q1-1.8
median0
Q31.79
95-th percentile5.15
Maximum72.45
Range128.14
Interquartile range (IQR)3.59

Descriptive statistics

Standard deviation3.43058
Coefficient of variation (CV)-117.77429
Kurtosis7.9667826
Mean-0.02912843
Median Absolute Deviation (MAD)1.79
Skewness-0.33387524
Sum-234962.5
Variance11.768879
MonotonicityNot monotonic
2024-01-23T13:53:26.711093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17 13197
 
0.2%
0.03 13147
 
0.2%
-0.12 13127
 
0.2%
-0.01 13104
 
0.2%
-0.2 13099
 
0.2%
0.12 13083
 
0.2%
0.23 13078
 
0.2%
0.01 13055
 
0.2%
0.2 13050
 
0.2%
-0.04 13046
 
0.2%
Other values (7000) 7935446
94.6%
(Missing) 325888
 
3.9%
ValueCountFrequency (%)
-55.69 1
< 0.1%
-55.57 1
< 0.1%
-54.99 1
< 0.1%
-54.94 1
< 0.1%
-54.93 1
< 0.1%
-54.67 1
< 0.1%
-54.46 1
< 0.1%
-54.42 1
< 0.1%
-54.36 1
< 0.1%
-54.27 1
< 0.1%
ValueCountFrequency (%)
72.45 1
< 0.1%
52.86 1
< 0.1%
52.54 1
< 0.1%
52.31 1
< 0.1%
51.6 1
< 0.1%
50.8 1
< 0.1%
50.52 1
< 0.1%
50.05 1
< 0.1%
49.58 1
< 0.1%
48.59 1
< 0.1%

theta_gsm
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17965
Distinct (%)0.2%
Missing325888
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean0.19676914
Minimum-89.91
Maximum89.96
Zeros928
Zeros (%)< 0.1%
Negative4027157
Negative (%)48.0%
Memory size64.0 MiB
2024-01-23T13:53:26.977222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-89.91
5-th percentile-55.54
Q1-22.79
median0.06
Q323.13
95-th percentile56.41
Maximum89.96
Range179.87
Interquartile range (IQR)45.92

Descriptive statistics

Standard deviation33.278891
Coefficient of variation (CV)169.12657
Kurtosis-0.37915177
Mean0.19676914
Median Absolute Deviation (MAD)22.96
Skewness0.010439579
Sum1587224.9
Variance1107.4846
MonotonicityNot monotonic
2024-01-23T13:53:27.232391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.52 1060
 
< 0.1%
1.78 1053
 
< 0.1%
-5.61 1041
 
< 0.1%
-4.38 1041
 
< 0.1%
1.17 1040
 
< 0.1%
-1.45 1035
 
< 0.1%
-0.59 1030
 
< 0.1%
-6.89 1029
 
< 0.1%
0.4 1028
 
< 0.1%
-5.91 1028
 
< 0.1%
Other values (17955) 8056047
96.0%
(Missing) 325888
 
3.9%
ValueCountFrequency (%)
-89.91 1
 
< 0.1%
-89.89 1
 
< 0.1%
-89.85 1
 
< 0.1%
-89.82 3
< 0.1%
-89.79 1
 
< 0.1%
-89.78 1
 
< 0.1%
-89.77 2
< 0.1%
-89.76 3
< 0.1%
-89.74 1
 
< 0.1%
-89.73 2
< 0.1%
ValueCountFrequency (%)
89.96 1
< 0.1%
89.93 1
< 0.1%
89.91 1
< 0.1%
89.89 1
< 0.1%
89.87 1
< 0.1%
89.85 1
< 0.1%
89.84 1
< 0.1%
89.83 2
< 0.1%
89.82 2
< 0.1%
89.81 2
< 0.1%

phi_gsm
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36001
Distinct (%)0.4%
Missing326388
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean194.37122
Minimum0
Maximum360
Zeros100
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size64.0 MiB
2024-01-23T13:53:27.512966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.3
Q1124.69
median177.66
Q3286.56
95-th percentile340.84
Maximum360
Range360
Interquartile range (IQR)161.87

Descriptive statistics

Standard deviation96.828179
Coefficient of variation (CV)0.49816109
Kurtosis-1.0575693
Mean194.37122
Median Absolute Deviation (MAD)77.69
Skewness-0.032815425
Sum1.567785 × 109
Variance9375.6962
MonotonicityNot monotonic
2024-01-23T13:53:27.770608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142.44 495
 
< 0.1%
144.69 484
 
< 0.1%
143.03 478
 
< 0.1%
142.36 478
 
< 0.1%
153.61 476
 
< 0.1%
142.17 476
 
< 0.1%
141.96 472
 
< 0.1%
143.35 471
 
< 0.1%
151.83 470
 
< 0.1%
139.47 469
 
< 0.1%
Other values (35991) 8061163
96.1%
(Missing) 326388
 
3.9%
ValueCountFrequency (%)
0 100
< 0.1%
0.01 171
< 0.1%
0.02 144
< 0.1%
0.03 198
< 0.1%
0.04 195
< 0.1%
0.05 172
< 0.1%
0.06 193
< 0.1%
0.07 174
< 0.1%
0.08 183
< 0.1%
0.09 171
< 0.1%
ValueCountFrequency (%)
360 80
< 0.1%
359.99 180
< 0.1%
359.98 171
< 0.1%
359.97 195
< 0.1%
359.96 182
< 0.1%
359.95 164
< 0.1%
359.94 193
< 0.1%
359.93 174
< 0.1%
359.92 171
< 0.1%
359.91 177
< 0.1%

bt
Real number (ℝ)

MISSING 

Distinct5135
Distinct (%)0.1%
Missing325888
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean5.6062266
Minimum0.03
Maximum80.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.0 MiB
2024-01-23T13:53:28.059960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile2.28
Q13.64
median4.95
Q36.72
95-th percentile11.11
Maximum80.53
Range80.5
Interquartile range (IQR)3.08

Descriptive statistics

Standard deviation3.1100016
Coefficient of variation (CV)0.55474062
Kurtosis18.7539
Mean5.6062266
Median Absolute Deviation (MAD)1.48
Skewness2.8092996
Sum45222246
Variance9.6721102
MonotonicityNot monotonic
2024-01-23T13:53:28.331366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.84 17151
 
0.2%
3.8 17034
 
0.2%
4.06 16803
 
0.2%
3.76 16645
 
0.2%
3.59 16553
 
0.2%
3.96 16526
 
0.2%
4.23 16519
 
0.2%
4.31 16433
 
0.2%
3.55 16405
 
0.2%
3.72 16404
 
0.2%
Other values (5125) 7899959
94.1%
(Missing) 325888
 
3.9%
ValueCountFrequency (%)
0.03 2
 
< 0.1%
0.05 1
 
< 0.1%
0.07 3
 
< 0.1%
0.08 4
 
< 0.1%
0.09 5
 
< 0.1%
0.1 1
 
< 0.1%
0.11 5
 
< 0.1%
0.12 7
< 0.1%
0.13 7
< 0.1%
0.14 17
< 0.1%
ValueCountFrequency (%)
80.53 1
< 0.1%
79.71 1
< 0.1%
78.78 1
< 0.1%
73.69 1
< 0.1%
72.58 1
< 0.1%
72.51 1
< 0.1%
72.11 1
< 0.1%
71.97 1
< 0.1%
71.68 1
< 0.1%
71.64 1
< 0.1%

density
Real number (ℝ)

MISSING 

Distinct7481
Distinct (%)0.1%
Missing684890
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean4.4217257
Minimum0
Maximum199.7
Zeros3704
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size64.0 MiB
2024-01-23T13:53:28.619230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.58
Q11.79
median3.34
Q35.71
95-th percentile11.61
Maximum199.7
Range199.7
Interquartile range (IQR)3.92

Descriptive statistics

Standard deviation4.3319107
Coefficient of variation (CV)0.97968778
Kurtosis75.843437
Mean4.4217257
Median Absolute Deviation (MAD)1.82
Skewness5.0431686
Sum34080142
Variance18.76545
MonotonicityNot monotonic
2024-01-23T13:53:28.896949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 15634
 
0.2%
1.5 15317
 
0.2%
2.1 15247
 
0.2%
1.6 14857
 
0.2%
2.4 14844
 
0.2%
1.75 14774
 
0.2%
1.9 14710
 
0.2%
2.04 14687
 
0.2%
1.8 14668
 
0.2%
2.08 14607
 
0.2%
Other values (7471) 7558085
90.1%
(Missing) 684890
 
8.2%
ValueCountFrequency (%)
0 3704
< 0.1%
0.01 3549
< 0.1%
0.02 2341
< 0.1%
0.03 2635
< 0.1%
0.04 3170
< 0.1%
0.05 3230
< 0.1%
0.06 4769
0.1%
0.07 5403
0.1%
0.08 4687
0.1%
0.09 5303
0.1%
ValueCountFrequency (%)
199.7 1
< 0.1%
195.26 1
< 0.1%
193.47 1
< 0.1%
192.72 1
< 0.1%
192.23 1
< 0.1%
191.68 1
< 0.1%
191.46 1
< 0.1%
190.79 1
< 0.1%
190.53 1
< 0.1%
190.31 1
< 0.1%

speed
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct64036
Distinct (%)0.8%
Missing689555
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean430.5854
Minimum0
Maximum1198.49
Zeros628
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size64.0 MiB
2024-01-23T13:53:29.177979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile300.9
Q1356.8
median409.7
Q3485.66
95-th percentile628.5
Maximum1198.49
Range1198.49
Interquartile range (IQR)128.86

Descriptive statistics

Standard deviation100.57773
Coefficient of variation (CV)0.23358369
Kurtosis0.72396492
Mean430.5854
Median Absolute Deviation (MAD)60.65
Skewness0.9092821
Sum3.3166982 × 109
Variance10115.879
MonotonicityNot monotonic
2024-01-23T13:53:29.444864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
379.1 1213
 
< 0.1%
376.8 1210
 
< 0.1%
378.2 1208
 
< 0.1%
377.6 1188
 
< 0.1%
378.5 1186
 
< 0.1%
380.4 1183
 
< 0.1%
379.8 1181
 
< 0.1%
378.7 1176
 
< 0.1%
368.5 1176
 
< 0.1%
378.4 1176
 
< 0.1%
Other values (64026) 7690868
91.6%
(Missing) 689555
 
8.2%
ValueCountFrequency (%)
0 628
< 0.1%
224.72 7
 
< 0.1%
224.96 1
 
< 0.1%
224.98 1
 
< 0.1%
224.99 2
 
< 0.1%
225.05 1
 
< 0.1%
225.06 4
 
< 0.1%
225.08 1
 
< 0.1%
225.1 2
 
< 0.1%
225.12 3
 
< 0.1%
ValueCountFrequency (%)
1198.49 1
< 0.1%
1166.53 1
< 0.1%
1159.7 1
< 0.1%
1152.7 1
< 0.1%
1151.4 2
< 0.1%
1148.5 1
< 0.1%
1147.84 1
< 0.1%
1147 1
< 0.1%
1145.9 1
< 0.1%
1144.1 1
< 0.1%

temperature
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct541646
Distinct (%)7.1%
Missing811768
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean115096.76
Minimum0
Maximum6223700
Zeros646
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size64.0 MiB
2024-01-23T13:53:29.726359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16123
Q139787
median77371
Q3151220
95-th percentile326725.45
Maximum6223700
Range6223700
Interquartile range (IQR)111433

Descriptive statistics

Standard deviation120312.04
Coefficient of variation (CV)1.0453122
Kurtosis33.961546
Mean115096.76
Median Absolute Deviation (MAD)46099
Skewness3.7865275
Sum8.7249694 × 1011
Variance1.4474987 × 1010
MonotonicityNot monotonic
2024-01-23T13:53:29.988918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 646
 
< 0.1%
108000 229
 
< 0.1%
105000 226
 
< 0.1%
106000 224
 
< 0.1%
109000 222
 
< 0.1%
121000 211
 
< 0.1%
123000 211
 
< 0.1%
125000 207
 
< 0.1%
111000 207
 
< 0.1%
114000 206
 
< 0.1%
Other values (541636) 7577963
90.3%
(Missing) 811768
 
9.7%
ValueCountFrequency (%)
0 646
< 0.1%
1496 1
 
< 0.1%
1632 1
 
< 0.1%
1829 1
 
< 0.1%
1879 1
 
< 0.1%
1915 1
 
< 0.1%
1922 1
 
< 0.1%
1925 1
 
< 0.1%
1980 1
 
< 0.1%
1983 1
 
< 0.1%
ValueCountFrequency (%)
6223700 1
< 0.1%
6046900 1
< 0.1%
5751308 1
< 0.1%
5543300 1
< 0.1%
5387600 1
< 0.1%
5052500 1
< 0.1%
5024886 1
< 0.1%
4206672 1
< 0.1%
4035915 1
< 0.1%
4016337 1
< 0.1%

source
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing316816
Missing (%)3.8%
Memory size64.0 MiB
ac
6630304 
ds
1445200 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters16151008
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowac
2nd rowac
3rd rowac
4th rowac
5th rowac

Common Values

ValueCountFrequency (%)
ac 6630304
79.0%
ds 1445200
 
17.2%
(Missing) 316816
 
3.8%

Length

2024-01-23T13:53:30.234256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-23T13:53:30.454461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ac 6630304
82.1%
ds 1445200
 
17.9%

Most occurring characters

ValueCountFrequency (%)
a 6630304
41.1%
c 6630304
41.1%
d 1445200
 
8.9%
s 1445200
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16151008
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6630304
41.1%
c 6630304
41.1%
d 1445200
 
8.9%
s 1445200
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 16151008
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6630304
41.1%
c 6630304
41.1%
d 1445200
 
8.9%
s 1445200
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16151008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6630304
41.1%
c 6630304
41.1%
d 1445200
 
8.9%
s 1445200
 
8.9%

Interactions

2024-01-23T13:50:35.381261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:40:55.519188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:44.361070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:30.564397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:18.608516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:04.941499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:50.674364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:32.940233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:16.538907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:00.607557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:44.801967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:28.546128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:12.004093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:54.434813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:38.294424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:40:59.268942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:47.477882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:33.752628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:22.106769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:08.201038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:53.809511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:36.016567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:19.690977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:03.843792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:47.926941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:31.684330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:15.318722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:57.417213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:41.184757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:02.910372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:50.695152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:37.046782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:25.376770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:11.504438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:57.001512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:39.086223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:22.918204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:06.976924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:50.956389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:34.691787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:18.628810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:00.370117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:44.082286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:06.161382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:53.912982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:40.341449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:28.563618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:14.974561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:00.191207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:42.238235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:26.173359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:10.179126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:54.248183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:37.989617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:21.693703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:03.297984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:46.950381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:09.635447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:57.359243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:43.718121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:32.045469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:18.199657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:03.153941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:45.467422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:29.407884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:13.402617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:57.381179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:41.137211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:24.662682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:06.251426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:49.910672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:13.137488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:00.929288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:47.190005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:35.491968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:21.486316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:06.122502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:48.647664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:32.437463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:16.591660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:00.685816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:44.462464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:27.724621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:09.165313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:52.802962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:16.816030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:04.101564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:50.953704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:38.774944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:24.781063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:09.072532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:51.663517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:35.532077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:19.793528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:03.827195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:47.581908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:30.720493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:12.081696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:55.642648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:20.699186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:07.402038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:54.423410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:42.092676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:28.105327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:12.089345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:54.724702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:38.590189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:22.929344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:06.992271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:50.665362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:33.614751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:14.971457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:58.494085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:23.752225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:10.600805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:57.855583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:45.457535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:31.460145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:15.025138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:57.858700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:41.622634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:26.016072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:10.233252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:53.717869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:36.616564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:17.923691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:51:01.365862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:27.085824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:13.971729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:01.371298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:48.841282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:34.627580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:18.044822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:00.928762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:44.771857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:29.150792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:13.252794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:56.917112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:40.026270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:20.890215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:51:04.312647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:31.180558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:17.359947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:05.064263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:52.264292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:38.061943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:21.136248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:03.981893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:47.971596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:32.383189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:16.390847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:59.966196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:42.947548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:23.802269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:51:07.207138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:35.075646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:20.753102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:08.671992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:55.407763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:41.174700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:24.059789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:07.015891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:51.114527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:35.441097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:19.390516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:03.009160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:45.662542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:26.790682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:51:10.155638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:38.043308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:23.986748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:11.795429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:58.568048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:44.322636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:27.030319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:10.162797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:54.297824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:38.628698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:22.459782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:05.989075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:48.627701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:29.657169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:51:12.856843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:41:41.100512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:42:27.141448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:43:14.999767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:01.651655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:44:47.442189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:45:29.930141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:13.333819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:46:57.339455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:47:41.660764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:48:25.491568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:08.948454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:49:51.451985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-23T13:50:32.534290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-01-23T13:53:30.699943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
btbx_gsebx_gsmby_gseby_gsmbz_gsebz_gsmdensityperiodphi_gsephi_gsmsourcespeedtemperaturetheta_gsetheta_gsm
bt1.000-0.187-0.1870.0830.078-0.018-0.0210.2630.126-0.042-0.0430.0680.1880.232-0.022-0.026
bx_gse-0.1871.0001.000-0.403-0.3940.0740.0710.0170.0790.3160.3120.070-0.013-0.0110.0630.060
bx_gsm-0.1871.0001.000-0.402-0.3930.0740.0710.0170.0790.3150.3110.070-0.013-0.0110.0630.060
by_gse0.083-0.403-0.4021.0000.963-0.058-0.059-0.0020.087-0.768-0.7400.0440.0290.023-0.049-0.053
by_gsm0.078-0.394-0.3930.9631.000-0.052-0.052-0.0030.059-0.739-0.7680.0360.0260.022-0.042-0.045
bz_gse-0.0180.0740.074-0.058-0.0521.0000.910-0.0020.1000.0410.0380.052-0.005-0.0030.9670.888
bz_gsm-0.0210.0710.071-0.059-0.0520.9101.000-0.0140.0910.0430.0390.048-0.017-0.0130.8870.965
density0.2630.0170.017-0.002-0.003-0.002-0.0141.0000.036-0.021-0.0190.038-0.102-0.005-0.009-0.022
period0.1260.0790.0790.0870.0590.1000.0910.0361.0000.0290.0290.593-0.049-0.0520.0230.033
phi_gse-0.0420.3160.315-0.768-0.7390.0410.043-0.0210.0291.0000.8530.076-0.0150.0280.0350.041
phi_gsm-0.0430.3120.311-0.740-0.7680.0380.039-0.0190.0290.8531.0000.077-0.0140.0270.0300.036
source0.0680.0700.0700.0440.0360.0520.0480.0380.5930.0760.0771.0000.0500.148-0.005-0.021
speed0.188-0.013-0.0130.0290.026-0.005-0.017-0.102-0.049-0.015-0.0140.0501.0000.746-0.005-0.017
temperature0.232-0.011-0.0110.0230.022-0.003-0.013-0.005-0.0520.0280.0270.1480.7461.000-0.005-0.017
theta_gse-0.0220.0630.063-0.049-0.0420.9670.887-0.0090.0230.0350.030-0.005-0.005-0.0051.0000.921
theta_gsm-0.0260.0600.060-0.053-0.0450.8880.965-0.0220.0330.0410.036-0.021-0.017-0.0170.9211.000

Missing values

2024-01-23T13:51:17.082952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-23T13:51:38.462637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-23T13:52:45.082589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

periodtimedeltabx_gseby_gsebz_gsetheta_gsephi_gsebx_gsmby_gsmbz_gsmtheta_gsmphi_gsmbtdensityspeedtemperaturesource
0train_a0 days 00:00:00-5.553.001.2511.09153.37-5.553.001.2511.09153.376.801.53383.92110237.0ac
1train_a0 days 00:01:00-5.583.161.1710.10151.91-5.583.161.1710.10151.916.831.69381.79123825.0ac
2train_a0 days 00:02:00-5.153.660.857.87146.04-5.153.660.857.87146.046.771.97389.1182548.0ac
3train_a0 days 00:03:00-5.203.680.686.17146.17-5.203.680.686.17146.176.741.97389.1182548.0ac
4train_a0 days 00:04:00-5.123.680.494.62145.72-5.123.680.494.62145.726.651.77384.2694269.0ac
5train_a0 days 00:05:00-4.783.860.050.80142.47-4.783.860.050.80142.476.540.93367.9828227.0ac
6train_a0 days 00:06:00-5.153.530.524.80147.06-5.153.530.524.80147.066.631.74384.7287302.0ac
7train_a0 days 00:07:00-5.353.061.2110.81152.06-5.353.061.2110.81152.066.651.70382.41101225.0ac
8train_a0 days 00:08:00-5.772.690.736.66156.36-5.772.690.736.66156.366.701.10357.3679922.0ac
9train_a0 days 00:09:00-6.072.200.272.65161.24-6.072.200.272.65161.246.801.77380.53125156.0ac
periodtimedeltabx_gseby_gsebz_gsetheta_gsephi_gsebx_gsmby_gsmbz_gsmtheta_gsmphi_gsmbtdensityspeedtemperaturesource
8392310train_c2435 days 23:50:00-1.692.30-3.55-51.14126.29-1.693.18-2.80-37.85117.944.563.24345.5228332.0ac
8392311train_c2435 days 23:51:00-1.752.28-3.57-51.13127.55-1.753.16-2.82-38.02118.944.583.96345.2928885.0ac
8392312train_c2435 days 23:52:00-1.091.95-3.90-60.21119.22-1.082.93-3.23-45.99110.334.494.06346.6326783.0ac
8392313train_c2435 days 23:53:00-0.761.66-4.07-65.89114.70-0.762.69-3.48-51.21105.724.464.25348.6925860.0ac
8392314train_c2435 days 23:54:00-0.561.48-4.14-69.05110.53-0.552.54-3.59-54.07102.264.434.36351.3333577.0ac
8392315train_c2435 days 23:55:00-1.182.00-3.92-59.28120.51-1.172.98-3.23-45.24111.484.564.64348.7323368.0ac
8392316train_c2435 days 23:56:00-1.342.12-3.83-56.78122.23-1.333.08-3.12-42.95113.454.584.27346.3626497.0ac
8392317train_c2435 days 23:57:00-1.622.33-3.63-51.98124.76-1.613.22-2.87-38.57116.584.613.95344.1227050.0ac
8392318train_c2435 days 23:58:00-2.272.48-3.22-43.81132.50-2.263.25-2.44-31.61124.814.652.81338.5233257.0ac
8392319train_c2435 days 23:59:00-2.152.41-3.30-45.55131.76-2.153.21-2.53-33.21123.754.622.30342.3132267.0ac